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METHODS OF RANDOMIZATION

IN CLINICAL TRIALS

NAME: Thaneshwar verma

ROLL N.O: 140604009

DEPT: Department of Pharmacology

GUIDE: Dr.Anoop Kishore

Ph.D , M. Pharm

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Contents:

1. Definition of randomization 3

2. Features of randomization 3-4

3. Advantages of randomization 4

4. Drawbacks of randomization 4

5. Types of randomization

Simple randomization 5-6

Permuted block randomization 6-7

Stratified randomization 8-9

Covariant adaptive randomization 10-11

6. Flowchart for randomization 12

7. References 13

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Randomization

A study in which people are allocated at random to receive one of several interventions. They are

considered the gold standard for demonstrating the effectiveness of a new medication, because

they provide unbiased estimates of effect.

Randomization is the process of assigning participants to treatment and control groups, assuming

that each participant has an equal chance of being assigned to any group. The first randomized

clinical trial conducted was on tuberculosis for streptomycin drug.

Two processes are involved in randomizing patient:

a. First is choosing a procedure to generate a random and unpredictable sequence of allocation.

b. Second and more practical is Allocation concealment, which refers to stringent precautions taken

to ensure that, group assignment of patient are not revealed to the study investigator prior to

allocation.

Features of randomization

1. Unpredictability

Each participant has the same chance of receiving any of the interventions. Allocation is carried

out using a chance mechanism so that neither the participant nor the investigator will know in

advance which will be assigned.

2. Balance

Treatment groups are of a similar size &constitution, groups are alike in all important aspects

and only differ in the intervention each group receives.

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3. Simplicity

Easy for investigator/staff to implement.

Advantages of using an Randomization include:

Random assignment ensures that known and unknown person and environment characteristics that

could affect the outcome of interest are evenly distributed across conditions.

Random assignment equalizes the influence of nonspecific processes not integral to the intervention

whose impact is being tested. Nonspecific processes might include effects of participating in a study,

being assessed, receiving attention.

True drawbacks of conducting an Randomization are:

They are time- and energy- intensive

They are expensive

They may not be feasible for all interventions or settings (e.g., some institutions have policies that

prohibit random assignment)

Types of randomization

1) Simple randomization

2) Permuted block randomization

3) Stratified randomization

4) Covariant adaptive randomization

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1) Simple randomization

Randomization based on a single sequence of random assignments is known as simple

randomization. This technique maintains complete randomness of the assignment of a person to a

particular group. The most common and basic method of simple randomization is flipping a coin.

For example, with 2 treatment groups (control versus treatment), the side of the coin (i.e. heads =

control, tails = treatment) determines the assignment of each participant.

Other methods include using a shuffled deck of cards (e.g. even = control, odd = treatment) or

throwing a die (e.g. below and equal to 3 = control, over 3 = treatment). A random number table

found in a statistics book or computer-generated random numbers can also be used for simple

randomization of participants. This randomization approach is simple and easy to implement in a

clinical trial. In large trials (n >200), simple randomization can be trusted to generate similar

numbers of participants among groups. However, randomization results could be problematic in

relatively small sample size clinical trials (n < 100), resulting in an unequal number of participants

among groups. For example, using a coin toss with a small sample size (n = 10) may result in an

imbalance such that 7 participants are assigned to the control group and 3 to the treatment group.

Imbalance of sample size between treatment arms due to simple randomization (coin

toss) in a small trial (n = 10).

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2) Permuted block randomisation

The block randomization method is designed to randomize participants into groups that result in

equal sample sizes. This method is used to ensure a balance in sample size across groups over time.

Blocks are small and balanced with predetermined group assignments, which keeps the numbers of

participants in each group similar at all times.

The block size is determined by the researcher and should be a multiple of the number of Groups

(i.e. with 2 treatment groups, block size of either 4 or 6). Blocks are best used in smaller increments

as researchers can more easily control balance. After block size has been determined, all possible

balanced combinations of assignment within the block (i.e. equal number for all groups within the

block) must be calculated. Blocks are then randomly chosen to determine the participants

assignment into the groups. For a clinical trial with control and treatment groups involving 40

participants, a randomized block procedure would be as follows:

(1) A block size of 4 is chosen

(2) Possible balanced combinations with 2 C (control) and 2 T(treatment) subjects are calculated as

6 (TTCC, TCTC,TCCT, CTTC, CTCT, CCTT), and (3) blocks are randomly chosen to determine the

assignment of all 40 participants (e.g. one random sequence would be [TTCC /TCCT / CTTC / CTTC /

TCCT / CCTT / TTCC / TCTC /CTCT / TCTC]). This procedure results in 20 participants in both the

control and treatment groups. Although balance in sample size may be achieved with this method,

groups may be generated that are rarely comparable in terms of certain covariates. For example,

one group may have more participants with secondary diseases (e.g. diabetes, multiple sclerosis,

cancer) that could confound the data and may negatively influence the results of the clinical trial.

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3) Stratified randomization

The stratified randomization method addresses the need to control and balance the influence of

covariates. This method can be used to achieve balance among groups in terms of participants

baseline characteristics (covariates).Specific covariates must be identified by the researcher who

understands the potential influence each covariate has on the dependent variable. Stratified

randomization is achieved by generating a separate block for each combination of covariates, and

participants are assigned to the appropriate block of covariates. After all participants have been

identified and assigned into blocks, simple randomization occurs within each block to assign

participants to one of the groups.

The stratified randomization method controls for the possible influence of covariates that

would jeopardize the conclusions of the clinical trial. For example, a clinical trial of different

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rehabilitation techniques after a surgical procedure will have a number of covariates. It is

well known that the age of the patient affects the rate of healing. Thus, age could be a confounding

variable and influence the outcome of the clinical trial. Stratified randomization can balance the

control and treatment groups for age or other identified covariates.

For example, with 2 groups involving 40 participants, the stratified randomization method might be

used to control the covariates of sex (2 levels: male, female) and body mass index (3 levels:

underweight, normal, overweight) between study arms. With these 2 covariates, possible block

combinations total 6 (eg, male, underweight).

A simple randomization procedure, such as flipping a coin, is used to assign the participants Within

each block to one of the treatment groups. Although stratified randomization is a relatively simple

and useful technique, especially for smaller clinical trials, it becomes complicated to implement if

many covariates must be controlled. For example, too many block combinations may lead to

imbalances in overall treatment allocations because a large number of blocks can generate small

participant numbers within the block. Balance in covariates begins to fail when the number of blocks

approaches half the sample size. If another 4-level covariate was added to the example, the number

of block combinations would increase from 6 to 24, for an average of fewer than 2 participants per

block, reducing the usefulness of the procedure to balance the covariates and jeopardizing the

validity of the clinical trial. In small studies, it may not be feasible to stratify more than 1 or 2

covariates because the number of blocks can quickly approach the number of participants.

Stratified randomization has another limitation: it works only when all participants have been

identified before group assignment. This method is rarely applicable, however, because clinical trial

participants are often enrolled one at a time on a continuous basis. When baseline characteristics

of all participants are not available before assignment, using stratified randomization is difficult.

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Stratified randomization procedure produces equalized study groups that are balanced

by covariates.

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4) Covariant adaptive randomization

Covariate adaptive randomization has been recommended by many researchers as a valid

alternative randomization method for clinical trials. In covariate adaptive randomization, a new

participant is sequentially assigned to a particular treatment group by taking into account the

specific covariates and previous assignments of participants.

Covariate adaptive randomization uses the method of minimization by assessing the imbalance of

sample size among several covariates.

This covariate adaptive approach was first described by Taves. The Taves covariate adaptive

randomization method allows for the examination of previous participant group assignments to

make a case-by-case decision on group assignment for each individual who enrolls in the study.

Consider again the example of 2 groups involving 40participants, with sex (2 levels: male, female)

and body mass index (3 levels: underweight, normal, overweight) as covariates. Assume the first 9

participants have already been randomly assigned to groups by flipping a coin. The 9 participants’

group assignments are broken down by covariate level. Now the 10th participant, who is male and

underweight, needs to be assigned to a group (i.e. control versus treatment). Based on the

characteristics of the 10th participant, the Taves method adds marginal totals of the corresponding

covariate categories for each group and compares the totals. The participant is assigned to the group

with the lower covariate total to minimize imbalance. In this example, the appropriate categories

are male and underweight, which results in the total of 3 (2 for male category + 1for underweight

category) for the control group and a total of 5 (3 for male category + 2 for underweight category)

for the treatment group. Because the sum of marginal totals is lower for the control group (3, 5),

the 10th participant is assigned to the control group.

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Flowchart for selecting appropriate randomization technique

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References

• Issues in Outcomes Research: An Overview of Randomization Techniques for Clinical

Trials[Internet][cited on 20-1-2012]. Available from URL:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2267325/

• General Considerations For Clinical Trials E8[Internet][cited on 16-1-2012]. Available from

URL:

http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E8/S

tep4/E8_Guideline.pdf